import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

import torchvision
from timm import create_model
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
import math

from torch import optim
from torch.optim import AdamW
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from tqdm import tqdm


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
        self.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
        self.drop = nn.Dropout(drop)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        x = self.fc1(x)
        x = self.dwconv(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x




class LKA(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
        # self.conv_spatial = nn.Conv2d(dim, dim, 5, stride=1, padding=6, groups=dim, dilation=3)
        self.conv_spatial = nn.Conv2d(dim, dim, 7, stride=1, padding=9, groups=dim, dilation=3)
        self.conv1 = nn.Conv2d(dim, dim, 1)


    def forward(self, x):
        u = x.clone()        
        attn = self.conv0(x)
        attn = self.conv_spatial(attn)
        attn = self.conv1(attn)

        return u * attn


class Attention(nn.Module):
    def __init__(self, d_model):    #d_model:输入维度
        super().__init__()

        self.proj_1 = nn.Conv2d(d_model, d_model, 1)
        self.activation = nn.GELU()
        self.spatial_gating_unit = LKA(d_model)
        self.proj_2 = nn.Conv2d(d_model, d_model, 1)

    def forward(self, x):
        shorcut = x.clone()
        x = self.proj_1(x)
        x = self.activation(x)
        x = self.spatial_gating_unit(x)
        x = self.proj_2(x)
        x = x + shorcut
        return x


class Block(nn.Module):
    def __init__(self, dim, mlp_ratio=4., drop=0.,drop_path=0., act_layer=nn.GELU):
        super().__init__()
        self.norm1 = nn.BatchNorm2d(dim)
        self.attn = Attention(dim)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()   #模型的正则化

        self.norm2 = nn.BatchNorm2d(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        layer_scale_init_value = 1e-2            
        self.layer_scale_1 = nn.Parameter(
            layer_scale_init_value * torch.ones((dim)), requires_grad=True)     #用于控制残差连接中的缩放系数
        self.layer_scale_2 = nn.Parameter(
            layer_scale_init_value * torch.ones((dim)), requires_grad=True)

        self.apply(self._init_weights)  #初始化权重

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        x = x + self.drop_path(self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.attn(self.norm1(x)))
        x = x + self.drop_path(self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(self.norm2(x)))
        return x


class OverlapPatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """

    def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
    #def __init__(self, img_size=224, patch_size=1, stride=1, in_chans=3, embed_dim=768):
        super().__init__()
        patch_size = to_2tuple(patch_size)
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
                              padding=(patch_size[0] // 2, patch_size[1] // 2))
        self.norm = nn.BatchNorm2d(embed_dim)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        x = self.norm(x)        
        return x, H, W


class VAN(nn.Module):
    def __init__(self, img_size=224, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
                mlp_ratios=[4, 4, 4, 4], drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=[3, 4, 6, 3], num_stages=4, flag=False):
        super().__init__()
        if flag == False:
            self.num_classes = num_classes
        self.depths = depths
        self.num_stages = num_stages

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0

        for i in range(num_stages):
            #patch_embed = OverlapPatchEmbed(img_size=img_size if i == 0 else img_size // (2 ** (i + 1)),
            patch_embed = OverlapPatchEmbed(img_size=img_size if i == 0 else img_size // (2 ** (i + 1)),
                                            patch_size=7 if i == 0 else 3,
                                            stride=4 if i == 0 else 2,
                                            in_chans=in_chans if i == 0 else embed_dims[i - 1],
                                            embed_dim=embed_dims[i])

            block = nn.ModuleList([Block(
                dim=embed_dims[i], mlp_ratio=mlp_ratios[i], drop=drop_rate, drop_path=dpr[cur + j])
                for j in range(depths[i])])
            norm = norm_layer(embed_dims[i])
            cur += depths[i]

            setattr(self, f"patch_embed{i + 1}", patch_embed)
            setattr(self, f"block{i + 1}", block)
            setattr(self, f"norm{i + 1}", norm)

        # classification head
        self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def freeze_patch_emb(self):
        self.patch_embed1.requires_grad = False

    @torch.jit.ignore   #告诉TorchScript编译器忽略被装饰的函数或方法，不对其进行编译和优化，而直接使用原始的Python代码。
    def no_weight_decay(self):
        return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'}  # has pos_embed may be better

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        B = x.shape[0]

        for i in range(self.num_stages):
            patch_embed = getattr(self, f"patch_embed{i + 1}")
            block = getattr(self, f"block{i + 1}")
            norm = getattr(self, f"norm{i + 1}")
            x, H, W = patch_embed(x)
            for blk in block:
                x = blk(x)
            x = x.flatten(2).transpose(1, 2)
            x = norm(x)
            if i != self.num_stages - 1:
                x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

        return x.mean(dim=1)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)

        return x


class DWConv(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x):
        x = self.dwconv(x)
        return x


def _conv_filter(state_dict, patch_size=16):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    out_dict = {}
    for k, v in state_dict.items():
        if 'patch_embed.proj.weight' in k:
            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
        out_dict[k] = v

    return out_dict


model_urls = {
    "van_b0": "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar",
    "van_b1": "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar",
    "van_b2": "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar",
    "van_b3": "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar",
}


def load_model_weights(model, arch, kwargs):
    url = model_urls[arch]
    checkpoint = torch.hub.load_state_dict_from_url(
        url=url, map_location="cpu", check_hash=True
    )
    strict = True
    if "num_classes" in kwargs and kwargs["num_classes"] != 1000:
        strict = False
        del checkpoint["state_dict"]["head.weight"]
        del checkpoint["state_dict"]["head.bias"]
    model.load_state_dict(checkpoint["state_dict"], strict=strict)
    return model


@register_model #在模型库中注册自定义模型
def van_b0(pretrained=False, **kwargs):
    model = VAN(
        embed_dims=[32, 64, 160, 256], mlp_ratios=[8, 8, 4, 4],
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 3, 5, 2],
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        model = load_model_weights(model, "van_b0", kwargs)
    return model


@register_model
def van_b1(pretrained=False, **kwargs):
    model = VAN(
        embed_dims=[64, 128, 320, 512], mlp_ratios=[8, 8, 4, 4],
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 4, 2],
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        model = load_model_weights(model, "van_b1", kwargs)
    return model

@register_model
def van_b2(pretrained=False, **kwargs):
    model = VAN(
        embed_dims=[64, 128, 320, 512], mlp_ratios=[8, 8, 4, 4],
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 3, 12, 3],
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        model = load_model_weights(model, "van_b2", kwargs)
    return model

@register_model
def van_b3(pretrained=False, **kwargs):
    model = VAN(
        embed_dims=[64, 128, 320, 512], mlp_ratios=[8, 8, 4, 4],
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 5, 27, 3],
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        model = load_model_weights(model, "van_b3", kwargs)
    return model

@register_model
def van_b4(pretrained=False, **kwargs):
    model = VAN(
        embed_dims=[64, 128, 320, 512], mlp_ratios=[8, 8, 4, 4],
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3],
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        model = load_model_weights(model, "van_b4", kwargs)
    return model


@register_model
def van_b5(pretrained=False, **kwargs):
    model = VAN(
        embed_dims=[96, 192, 480, 768], mlp_ratios=[8, 8, 4, 4],
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 3, 24, 3],
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        model = load_model_weights(model, "van_b5", kwargs)
    return model


@register_model
def van_b6(pretrained=False, **kwargs):
    model = VAN(
        embed_dims=[96, 192, 384, 768], mlp_ratios=[8, 8, 4, 4],
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[6,6,90,6],
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        model = load_model_weights(model, "van_b6", kwargs)
    return model


def test_model():
    model = van_b0(drop_path_rate=0.1)

    model.load_state_dict(torch.load("vanb0_cifar100_300.pth"))
    # 在测试集上评估模型
    model.eval()
    test_loss = 0.0
    test_correct = 0
    test_total = 0

    with torch.no_grad():
        for inputs, targets in testloader:
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            test_loss += loss.item()
            _, predicted = outputs.max(1)
            test_total += targets.size(0)
            test_correct += predicted.eq(targets).sum().item()

    test_acc = 100. * test_correct / test_total
    avg_test_loss = test_loss / len(testloader)
    print(f"Epoch [{epoch + 1}/{num_epochs}], Test Loss: {avg_test_loss:.4f}, Test Accuracy: {test_acc:.2f}%")
    writer.add_scalar("Loss/test", avg_test_loss, epoch)
    writer.add_scalar("Accuracy/test", test_acc, epoch)




if __name__ == '__main__':

    # 定义数据预处理
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),   # 随机裁剪、随机水平翻转、转换为张量和归一化
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ])
    # 加载训练数据集和测试数据集
    trainset = torchvision.datasets.CIFAR100(root='./data/cifar100', train=True, download=True, transform=transform_train)
    trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

    testset = torchvision.datasets.CIFAR100(root='./data/cifar100', train=False, download=True, transform=transform_test)
    testloader = DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

    # 定义模型
    model = van_b0(pretrained=True,img_size=32,num_classes=100)
    #model = van_b0(pretrained=False, drop_path_rate=0.1, img_size=32, num_classes=100)
    #model.load_state_dict(torch.load("vanb0_cifar100.pth"))
    '''
     model = create_model(
        van_b0,
        pretrained=False,
        drop_rate=0,

        drop_block_rate=None,
    )
    '''

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)    # 将模型对象 model 中的所有参数和缓冲区移动到指定的设备 device 上

    # 定义损失函数和优化器
    criterion = nn.CrossEntropyLoss()   # 交叉熵损失函数
    #   momentum 是动量，用于加速优化过程。它可以在参数更新时考虑之前的更新方向，从而增加参数更新的惯性。
    #   weight_decay 是权重衰减（L2正则化），用于控制模型参数的大小。它可以防止模型过拟合，通过增加正则化项减小参数的幅度。
    #optimizer = optim.SGD(model.parameters(), lr=5e-4, momentum=0.9, weight_decay=5e-4)
    optimizer = AdamW(model.parameters(), lr=1e-3, betas=(0.9, 0.999), weight_decay=5e-2)
    #   betas 是 Adam 算法中的动量参数，默认值为 (0.9, 0.999);较小的动量可以减少模型在参数更新中的摆动
    # optimizer = AdamW(model.parameters(), lr=1e-3, betas=(0.9, 0.999), weight_decay=5e-4) # epoch=30 正确率：45%
    # optimizer = AdamW(model.parameters(), lr=1e-4, betas=(0.8, 0.999), weight_decay=5e-2)   #epoch=50 正确率：44% ，增加epoch可能还能更高
    # optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.8, weight_decay=5e-2)   # epoch 正确率：10%
    # optimizer = optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-2)   #epoch 正确率：10%
    # optimizer = optim.SGD(model.parameters(), lr=5e-4, momentum=0.9, weight_decay=5e-4)   #epoch：300 正确率：48%
    # optimizer = optim.SGD(model.parameters(), lr=5e-4, momentum=0.9, weight_decay=5e-4)   #epoch：600 正确率：58%
    # optimizer = AdamW(model.parameters(), lr=1e-3, betas=(0.9, 0.999), weight_decay=5e-2) num_classes=100,img_size=32 正确率：48%
    # optimizer = AdamW(model.parameters(), lr=1e-3, betas=(0.9, 0.999), weight_decay=5e-2) 去掉LKA，epoch:30 正确率：50.26%    29-21-36-41
    # 加上改变卷积核大小的LKA，不进行下采样，epoch：30，正确率：46.65%  30-10-51-53
    # 加上原始LKA，不进行下采样
    # 载入github预训练的模型


    # 设置训练参数
    num_epochs = 30
    log_interval = 100

    # 创建TensorBoard可视化对象
    writer = SummaryWriter()



    # 训练模型，模型设置为训练模式，启用与训练相关的模块，例如Dropout和BatchNormalization
    # 在训练模式下，Dropout层会以一定的概率将输入中的神经元置零，以减少过拟合。在测试模式下，Dropout层不会对输入进行任何修改。
    # 在训练模式下，BatchNormalization层会对输入进行归一化，并学习批次均值和方差的估计值。在测试模式下，BatchNormalization层使用在训练阶段学习的参数对输入进行归一化。
    model.train()

    for epoch in range(num_epochs):
        running_loss = 0.0  # 累计的损失值
        correct = 0         # 预测正确的样本数量
        total = 0           # 样本的总数量

        # tqdm(trainloader) 是将训练数据加载器 trainloader 作为参数传递给 tqdm 函数，可以实时地看到训练过程中的进度条，以及每个批次的处理速度
        for batch_idx, (inputs, targets) in enumerate(tqdm(trainloader)):
            inputs, targets = inputs.to(device), targets.to(device)     # 输入数据和目标标签
            optimizer.zero_grad()   # 清除优化器中之前的梯度，准备接收新的梯度。

            # 前向传播
            outputs = model(inputs)     # 通过模型进行前向传播，得到模型的预测输出。
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()

            # 统计训练信息
            running_loss += loss.item()     # 当前批次损失值标量
            _, predicted = outputs.max(1)   # 获取每个样本的预测结果中的最大值和对应的索引。
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

            # 打印训练信息
            if (batch_idx + 1) % log_interval == 0:
                acc = 100. * correct / total
                avg_loss = running_loss / log_interval
                print(
                    f"Epoch [{epoch + 1}/{num_epochs}], Step [{batch_idx + 1}/{len(trainloader)}], Loss: {avg_loss:.4f}, Accuracy: {acc:.2f}%")
                writer.add_scalar("Loss/train", avg_loss, epoch * len(trainloader) + batch_idx)
                writer.add_scalar("Accuracy/train", acc, epoch * len(trainloader) + batch_idx)

                running_loss = 0.0
                correct = 0
                total = 0

        # 在测试集上评估模型
        model.eval()
        test_loss = 0.0
        test_correct = 0
        test_total = 0

        with torch.no_grad():
            for inputs, targets in testloader:
                inputs, targets = inputs.to(device), targets.to(device)
                outputs = model(inputs)
                loss = criterion(outputs, targets)
                test_loss += loss.item()
                _, predicted = outputs.max(1)
                test_total += targets.size(0)
                test_correct += predicted.eq(targets).sum().item()

        test_acc = 100. * test_correct / test_total
        avg_test_loss = test_loss / len(testloader)
        print(f"Epoch [{epoch + 1}/{num_epochs}], Test Loss: {avg_test_loss:.4f}, Test Accuracy: {test_acc:.2f}%")
        writer.add_scalar("Loss/test", avg_test_loss, epoch)
        writer.add_scalar("Accuracy/test", test_acc, epoch)

    # 保存模型
    # 接收两个参数，第一个参数是要保存到对象，在此为模型的状态字典，包含了模型的可学习参数及其对应的权重值
    torch.save(model.state_dict(), "vanb0_cifar100.pth")
    writer.close()